Variational Interaction Information Maximization for Cross-domain Disentanglement

• URL: http://arxiv.org/abs/2012.04251v1
• Date: Tue, 8 Dec 2020 07:11:35 GMT
• Title: Variational Interaction Information Maximization for Cross-domain Disentanglement
• Authors: HyeongJoo Hwang, Geon-Hyeong Kim, Seunghoon Hong, Kee-Eung Kim
• Abstract summary: Cross-domain disentanglement is the problem of learning representations partitioned into domain-invariant and domain-specific representations. We cast the simultaneous learning of domain-invariant and domain-specific representations as a joint objective of multiple information constraints. We show that our model achieves the state-of-the-art performance in the zero-shot sketch based image retrieval task.
• Score: 34.08140408283391
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Cross-domain disentanglement is the problem of learning representations partitioned into domain-invariant and domain-specific representations, which is a key to successful domain transfer or measuring semantic distance between two domains. Grounded in information theory, we cast the simultaneous learning of domain-invariant and domain-specific representations as a joint objective of multiple information constraints, which does not require adversarial training or gradient reversal layers. We derive a tractable bound of the objective and propose a generative model named Interaction Information Auto-Encoder (IIAE). Our approach reveals insights on the desirable representation for cross-domain disentanglement and its connection to Variational Auto-Encoder (VAE). We demonstrate the validity of our model in the image-to-image translation and the cross-domain retrieval tasks. We further show that our model achieves the state-of-the-art performance in the zero-shot sketch based image retrieval task, even without external knowledge. Our implementation is publicly available at: https://github.com/gr8joo/IIAE

Related papers

• xTED: Cross-Domain Adaptation via Diffusion-Based Trajectory Editing [21.37585797507323]
  Cross-domain policy transfer methods mostly aim at learning domain correspondences or corrections to facilitate policy learning. We propose the Cross-Domain Trajectory EDiting framework that employs a specially designed diffusion model for cross-domain trajectory adaptation. Our proposed model architecture effectively captures the intricate dependencies among states, actions, and rewards, as well as the dynamics patterns within target data.
  arXiv  Detail & Related papers  (2024-09-13T10:07:28Z)
• PiPa: Pixel- and Patch-wise Self-supervised Learning for Domain Adaptative Semantic Segmentation [100.6343963798169]
  Unsupervised Domain Adaptation (UDA) aims to enhance the generalization of the learned model to other domains. We propose a unified pixel- and patch-wise self-supervised learning framework, called PiPa, for domain adaptive semantic segmentation.
  arXiv  Detail & Related papers  (2022-11-14T18:31:24Z)
• Multi-Modal Cross-Domain Alignment Network for Video Moment Retrieval [55.122020263319634]
  Video moment retrieval (VMR) aims to localize the target moment from an untrimmed video according to a given language query. In this paper, we focus on a novel task: cross-domain VMR, where fully-annotated datasets are available in one domain but the domain of interest only contains unannotated datasets. We propose a novel Multi-Modal Cross-Domain Alignment network to transfer the annotation knowledge from the source domain to the target domain.
  arXiv  Detail & Related papers  (2022-09-23T12:58:20Z)
• Variational Transfer Learning using Cross-Domain Latent Modulation [1.9662978733004601]
  We introduce a novel cross-domain latent modulation mechanism to a variational autoencoder framework so as to achieve effective transfer learning. Deep representations of the source and target domains are first extracted by a unified inference model and aligned by employing gradient reversal. The learned deep representations are then cross-modulated to the latent encoding of the alternative domain, where consistency constraints are also applied.
  arXiv  Detail & Related papers  (2022-05-31T03:47:08Z)
• AFAN: Augmented Feature Alignment Network for Cross-Domain Object Detection [90.18752912204778]
  Unsupervised domain adaptation for object detection is a challenging problem with many real-world applications. We propose a novel augmented feature alignment network (AFAN) which integrates intermediate domain image generation and domain-adversarial training. Our approach significantly outperforms the state-of-the-art methods on standard benchmarks for both similar and dissimilar domain adaptations.
  arXiv  Detail & Related papers  (2021-06-10T05:01:20Z)
• Cross-Domain Latent Modulation for Variational Transfer Learning [1.9212368803706577]
  We propose a cross-domain latent modulation mechanism within a variational autoencoders (VAE) framework to enable improved transfer learning. We apply the proposed model to a number of transfer learning tasks including unsupervised domain adaptation and image-to-image translation.
  arXiv  Detail & Related papers  (2020-12-21T22:45:00Z)
• Unsupervised Cross-domain Image Classification by Distance Metric Guided Feature Alignment [11.74643883335152]
  Unsupervised domain adaptation is a promising avenue which transfers knowledge from a source domain to a target domain. We propose distance metric guided feature alignment (MetFA) to extract discriminative as well as domain-invariant features on both source and target domains. Our model integrates class distribution alignment to transfer semantic knowledge from a source domain to a target domain.
  arXiv  Detail & Related papers  (2020-08-19T13:36:57Z)
• Adversarial Bipartite Graph Learning for Video Domain Adaptation [50.68420708387015]
  Domain adaptation techniques, which focus on adapting models between distributionally different domains, are rarely explored in the video recognition area. Recent works on visual domain adaptation which leverage adversarial learning to unify the source and target video representations are not highly effective on the videos. This paper proposes an Adversarial Bipartite Graph (ABG) learning framework which directly models the source-target interactions.
  arXiv  Detail & Related papers  (2020-07-31T03:48:41Z)
• Context-Aware Domain Adaptation in Semantic Segmentation [59.79348089240319]
  We consider the problem of unsupervised domain adaptation in the semantic segmentation. Existing methods mainly focus on adapting domain-invariant features (what to transfer) through adversarial learning. We propose a cross-attention mechanism based on self-attention to capture context dependencies between two domains and adapt transferable context.
  arXiv  Detail & Related papers  (2020-03-09T09:57:24Z)
• Latent Normalizing Flows for Many-to-Many Cross-Domain Mappings [76.85673049332428]
  Learned joint representations of images and text form the backbone of several important cross-domain tasks such as image captioning. We propose a novel semi-supervised framework, which models shared information between domains and domain-specific information separately. We demonstrate the effectiveness of our model on diverse tasks, including image captioning and text-to-image synthesis.
  arXiv  Detail & Related papers  (2020-02-16T19:49:30Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.